Mobile Robot Navigation and Obstacle-avoidance using ANFIS in Unknown Environment

  title={Mobile Robot Navigation and Obstacle-avoidance using ANFIS in Unknown Environment},
  author={Mohammed Algabri and Hassan Mathkour and Hedjar Ramdane},
  journal={International Journal of Computer Applications},
Navigation and obstacle avoidance in an unknown environment is proposed in this paper using hybrid neural network with fuzzy logic controller. The overall system is termed as Adaptive Neuro Fuzzy Inference System (ANFIS). ANFIS combines the benefits of fuzzy logic and neural networks for the purpose of achieving robotic navigation task. Simulation results are presented using Khepera Simulator (KiKs) within MATLAB environment. Moreover, experimental results are obtained using Khepera III… 
Mobile robot navigation based on adaptive neuro-fuzzy inerence ssystem with virtual target strategy
  • Junyi Lu, U. KinTak
  • Computer Science
    2017 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR)
  • 2017
An enhanced mobile robot navigation method based on Adaptive Neuro-Fuzzy Inference System (ANFIS) in unknown and static environments is presented and results show that trapezoidal function and more fuzzy rules lead to better training and navigation performance.
Navigation of non-holonomic mobile robot using neuro-fuzzy logic with integrated safe boundary algorithm
In this method of target seeking behaviour, the obstacle avoidance at every instant improves the performance of robot in navigation approach and proves that ANFIS with safe boundary algorithm yields better performance in navigation, in particular with curved/irregular obstacles.
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The development of an E-Puck mobile robot obstacle avoidance controller using a Hybrid Intelligent System (HIS) based on Fuzzy Logic and Artificial Neural Networks is presented.
TLBO-Based Adaptive Neurofuzzy Controller for Mobile Robot Navigation in a Strange Environment
The quality of the obtained results extracted from simulations affirms TLBO-based ANFIS as an efficient alternative method for solving the navigation problem of the mobile robot.
Analysis and Development of Computational Intelligence based Navigational Controllers for Multiple Mobile Robots
This dissertation investigates the advanced methodologies for both single and multiple mobile robots navigation in highly cluttered environments using computational intelligence approach and concludes that the proposed navigational methodologies can be effectively implemented to solve the path optimization problems of mobile robot in any complex environment.
Mobile Robot Navigation and Obstacle Avoidance Techniques: A Review
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Advanced control algorithms for mobile robot
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Neurofuzzy-Based Approach to Mobile Robot Navigation in Unknown Environments
  • Anmin Zhu, Simon X. Yang
  • Computer Science
    IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews)
  • 2007
A neurofuzzy-based approach is proposed, which coordinates the sensor information and robot motion together and can adequately sense the environment around, autonomously avoid static and moving obstacles, and generate reasonable trajectories toward the target in various situations without suffering from the "dead cycle" problems.
A Novel Mobile Robot Navigation System Using Neuro-Fuzzy Rule-Based Optimization Technique
A new novel approach to control the autonomous mobile robot that moved along a collision free trajectory until it reaches its target is proposed in this study. The approach taken here utilizes a
Fuzzy-Neuro based Navigational Strategy for Mobile Robot
Fuzzy-neural network is able to build comprehensive knowledge bases considering sensor-rich system with real time constraints by adaptive learning, rule extraction and insertion, and neural/fuzzy reasoning.
Mobile robot obstacle avoidance using short memory: a dynamic recurrent neuro-fuzzy approach
A dynamic recurrent neuro-fuzzy system (DRNFS) with short memory for obstacle avoidance of mobile robots in unknown environments through supervised learning from a set of obstacle-avoidance trajectories provided by a human driver.
Adaptive Neuro-Fuzzy Controler With Genetic Training For Mobile Robot Control
The use of adaptive techniques in the optimization of navigation of Khepera mobile robot in an unstructured and dynamic environment is investigated and an improved performance in the adaptive neuro-fuzzy controller is realised.
ANFIS: adaptive-network-based fuzzy inference system
  • J. Jang
  • Computer Science
    IEEE Trans. Syst. Man Cybern.
  • 1993
The architecture and learning procedure underlying ANFIS (adaptive-network-based fuzzy inference system) is presented, which is a fuzzy inference system implemented in the framework of adaptive
& quot ; Mobile robot obstacle avoidance using short memory : a dynamic recurrent neuro - fuzzy approach , & quot ; Transactions of the Institute of Measurement and Control
  • 2012